SynCron: Efficient Synchronization Support for Near-Data-Processing Architectures
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Abstract (arXiv)
Near-Data-Processing (NDP) architectures present a promising way to alleviate data movement costs and can provide significant performance and energy benefits to parallel applications. Typically, NDP architectures support several NDP units, each including multiple simple cores placed close to memory. To fully leverage the benefits of NDP and achieve high performance for parallel workloads, efficient synchronization among the NDP cores of a system is necessary. However, supporting synchronization in many NDP systems is challenging because they lack shared caches and hardware cache coherence support, which are commonly used for synchronization in multicore systems, and communication across different NDP units can be expensive. This paper comprehensively examines the synchronization problem in NDP systems, and proposes SynCron, an end-to-end synchronization solution for NDP systems. SynCron adds low-cost hardware support near memory for synchronization acceleration, and avoids the need for hardware cache coherence support. SynCron has three components: 1) a specialized cache memory structure to avoid memory accesses for synchronization and minimize latency overheads, 2) a hierarchical message-passing communication protocol to minimize expensive communication across NDP units of the system, and 3) a hardware-only overflow management scheme to avoid performance degradation when hardware resources for synchronization tracking are exceeded. We evaluate SynCron using a variety of parallel workloads, covering various contention scenarios. SynCron improves performance by 1.27× on average (up to 1.78×) under high-contention scenarios, and by 1.35× on average (up to 2.29×) under low-contention real applications, compared to state-of-the-art approaches. SynCron reduces system energy consumption by 2.08× on average (up to 4.25×).
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jan 2021.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 2; 2025: 1.
9 papers.
- 2024PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory Systemcited by 5
- 2022Machine Learning Training on a Real Processing-in-Memory Systemcited by 4
- 2022Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloudcited by 4
- 2021Demystifying BERT: Implications for Accelerator Designcited by 3
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory Systemcited by 2
- 2024PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architecturescited by 2
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databasescited by 1
- 2023TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systemscited by 1
- 2025DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architecturescited by 0
- 2025DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures19 Nov 2025
- 2024PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System10 Apr 2024
- 2024PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures26 Feb 2024
- 2023TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems3 Apr 2023
- 2022Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud19 Sep 2022
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System16 Jul 2022
- 2022Machine Learning Training on a Real Processing-in-Memory System13 Jun 2022
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases29 May 2022
- 2021Demystifying BERT: Implications for Accelerator Design14 Apr 2021